已发表论文

连锁反应:揭示医学生消极生活事件与抑郁症状之间的双向关系

 

Authors Li K, Ren X, Ren L, Tan X, Zhao M, Liu C, Luo X, Feng Z, Dai Q

Received 19 May 2023

Accepted for publication 26 July 2023

Published 28 August 2023 Volume 2023:16 Pages 3399—3412

DOI https://doi.org/10.2147/PRBM.S419991

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Mei-Chun Cheung

Background: Previous studies have explored the relationship between negative life events and depression, but little is known about the bidirectional relationship between negative life events and depression, particularly in specific groups of medical cadets.
Purpose: This study aimed to explore the relationship between negative life events and depressive symptoms among medical cadets during their four years of college.
Methods: An analysis of 4-wave longitudinal data collected from 2015– 2018 was conducted using a cross-lagged panel network (CLPN) model to explore the complex causal relationship between negative life events and depressive symptoms in medical cadets (N=433).
Results: We found differences in negative life events and depressive symptoms among medical cadets across four network models over four years of university. Nodes A-21, A-20, A-23 and A-24, and depressive symptoms D-6 showed greater lagged effect values.
Conclusion: Our findings suggest that there is a lagged and mutually causal interaction between negative life events and depressive symptoms in medical cadets over 4 years of college, but that the predictability of negative life events is more important. However, more attention needs to be paid to the predictive role of depressive symptoms, especially those in early life which are often overlooked. Our study provides new insights into the relationship between negative life events and depressive symptoms in university students and helps to refine strategies for prevention and intervention of depression.
Keywords: depression, negative life events, CLPN, longitude relationship, vicious circle, network analysis